Artificial intelligence for ultrasonography: unique opportunities and challenges

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works

Key challenges for delivering clinical impact with artificial intelligence

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136 · see citing works

Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes

Ting, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402 · see citing works

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002683 · Cited by 1,719 · see citing works

Deep Learning in Medical Imaging: General Overview

Lee, J., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017) · Korean Journal of Radiology · doi:10.3348/kjr.2017.18.4.570 · Cited by 1,287 · see citing works

Preparing Medical Imaging Data for Machine Learning

Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020) · Radiology · doi:10.1148/radiol.2020192224 · Cited by 1,020 · see citing works

Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction

Park, S. H., & Han, K. (2018) · Radiology · doi:10.1148/radiol.2017171920 · Cited by 894 · see citing works

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology

Tang, A., Tam, R. C., Cadrin-Chênevert, A., Guest, W. C., Chong, J., Barfett, J., Chepelev, L. L., Cairns, R., Mitchell, J. R., Cicero, M. D., et al. (2018) · Canadian Association of Radiologists Journal · doi:10.1016/j.carj.2018.02.002 · Cited by 524 · see citing works

Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide

Soffer, S., Ben-Cohen, A., Shimon, O., Amitai, M. M., Greenspan, H. K., & Klang, E. (2019) · Radiology · doi:10.1148/radiol.2018180547 · Cited by 514 · see citing works

Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers

Kim, D. W., Jang, H. Y., Kim, K. W., Shin, Y., & Park, S. H. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2019.0025 · Cited by 459 · see citing works

Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study

Li, X., Zhang, S., Zhang, Q., Wei, X., Pan, Y., Zhao, J., Xin, X., Qin, C., Wang, X., Li, J., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(18)30762-9 · Cited by 433 · see citing works

Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the <i>Radiology</i> Editorial Board

Bluemke, D. A., Moy, L., Bredella, M. A., Ertl-Wagner, B. B., Fowler, K. J., Goh, V. J., Halpern, E. F., Hess, C. P., Schiebler, M. L., & Weiss, C. R. (2020) · Radiology · doi:10.1148/radiol.2019192515 · Cited by 343 · see citing works

Presenting machine learning model information to clinical end users with model facts labels

Sendak, M. P., Gao, M., Brajer, N., & Balu, S. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0253-3 · Cited by 279 · see citing works

Framing the challenges of artificial intelligence in medicine

Yu, K.-H., & Kohane, I. S. (2019) · BMJ Quality &amp; Safety · doi:10.1136/bmjqs-2018-008551 · Cited by 233 · see citing works

Regulation of predictive analytics in medicine

Parikh, R. B., Obermeyer, Z., & Navathe, A. S. (2019) · Science · doi:10.1126/science.aaw0029 · Cited by 223 · see citing works

Predictive analytics in health care: how can we know it works?

Van Calster, B., Wynants, L., Timmerman, D., Steyerberg, E. W., & Collins, G. S. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz130 · Cited by 223 · see citing works

Understanding artificial intelligence based radiology studies: What is overfitting?

Mutasa, S., Sun, S., & Ha, R. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.04.025 · Cited by 216 · see citing works

Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning

Wang, Y., Choi, E. J., Choi, Y., Zhang, H., Jin, G. Y., & Ko, S. (2020) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2020.01.001 · Cited by 189 · see citing works

Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network

Lee, J. H., Joo, I., Kang, T. W., Paik, Y. H., Sinn, D. H., Ha, S. Y., Kim, K., Choi, C., Lee, G., Yi, J., et al. (2020) · European Radiology · doi:10.1007/s00330-019-06407-1 · Cited by 144 · see citing works

Artificial intelligence in breast ultrasound

Wu, G.-G., Zhou, L., Xu, J., Wang, J., Wei, Q., Deng, Y., Cui, X., & Dietrich, C. F. (2019) · World Journal of Radiology · doi:10.4329/wjr.v11.i2.19 · Cited by 119 · see citing works

Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning

Do, S., Song, K. D., & Chung, J. W. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2019.0312 · Cited by 114 · see citing works

Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators

Jeong, E. Y., Kim, H. L., Ha, E. J., Park, S. Y., Cho, Y. J., & Han, M. (2018) · European Radiology · doi:10.1007/s00330-018-5772-9 · Cited by 112 · see citing works

Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography

Choi, J. S., Han, B.-K., Ko, E. S., Bae, J. M., Ko, E. Y., Song, S. H., Kwon, M.-R., Shin, J. H., & Hahn, S. Y. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2018.0530 · Cited by 104 · see citing works

Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience

Yoo, Y. J., Ha, E. J., Cho, Y. J., Kim, H. L., Han, M., & Kang, S. Y. (2018) · Korean Journal of Radiology · doi:10.3348/kjr.2018.19.4.665 · Cited by 90 · see citing works

Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges

Hwang, E. J., & Park, C. M. (2020) · Korean Journal of Radiology · doi:10.3348/kjr.2019.0821 · Cited by 74 · see citing works

Artificial intelligence in musculoskeletal ultrasound imaging

Shin, Y., Yang, J., Lee, Y. H., & Kim, S. J. (2020) · ULTRASONOGRAPHY · doi:10.14366/usg.20080 · Cited by 73 · see citing works

Advancing the beneficial use of machine learning in health care and medicine: Toward a community understanding

Nevin, L. M., & Editors, O. B. O. T. P. M. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002708 · Cited by 58 · see citing works

Artificial intelligence in breast ultrasonography

Kim, J., Kim, H. J., Kim, C., & Kim, W. H. (2020) · ULTRASONOGRAPHY · doi:10.14366/usg.20117 · Cited by 52 · see citing works

Deep learning-based decision support system for the diagnosis of neoplastic gallbladder polyps on ultrasonography: Preliminary results

Jeong, Y., Kim, J. H., Chae, H.-D., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-64205-y · Cited by 51 · see citing works

Applications of machine learning and deep learning to thyroid imaging: where do we stand?

Ha, E. J., & Baek, J. H. (2020) · ULTRASONOGRAPHY · doi:10.14366/usg.20068 · Cited by 50 · see citing works

Evaluating artificial intelligence applications in clinical settings

Nsoesie, E. O. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.2658 · Cited by 48 · see citing works

Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency

Yi, J., Kang, H. K., Kwon, J.-H., Kim, K.-S., Park, M. H., Seong, Y. K., Kim, D. W., Ahn, B. E., Ha, K., Lee, J.-Y., et al. (2020) · ULTRASONOGRAPHY · doi:10.14366/usg.20102 · Cited by 48 · see citing works

Transforming Global Health with AI

Mehta, M. C., Katz, I. T., & Jha, A. K. (2020) · New England Journal of Medicine · doi:10.1056/nejmp1912079 · Cited by 37 · see citing works

Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review

Park, S. H., Kim, Y., Lee, J. Y., Yoo, S., & Kim, C. J. (2019) · Science Editing · doi:10.6087/kcse.164 · Cited by 36 · see citing works

Current status of deep learning applications in abdominal ultrasonography

Song, K. D. (2020) · ULTRASONOGRAPHY · doi:10.14366/usg.20085 · Cited by 24 · see citing works

Principles for evaluating the clinical implementation of novel digital healthcare devices

Park, S. H., Do, K., Choi, J., Sim, J. S., Yang, D. M., Eo, H., Woo, H., Lee, J. M., Jung, S. E., & Oh, J. H. (2018) · Journal of Korean Medical Association · doi:10.5124/jkma.2018.61.12.765 · Cited by 18 · see citing works

Application of Computer-Aided Diagnosis to the Sonographic Evaluation of Cervical Lymph Nodes

Zhang, J., Wang, Y., Yu, B., Shi, X., & Zhang, Y. (2015) · Ultrasonic Imaging · doi:10.1177/0161734615589080 · Cited by 12 · see citing works

Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester

Walker, M. C., Willner, I., Miguel, O. X., Murphy, M. S. Q., El-Chaâr, D., Moretti, F., Dingwall Harvey, A. L. J., Rennicks White, R., Muldoon, K. A., Carrington, A. M., et al. (2022) · PLOS ONE · doi:10.1371/journal.pone.0269323 · Cited by 31 · see citing works

Automated prediction of early spontaneous miscarriage based on the analyzing ultrasonographic gestational sac imaging by the convolutional neural network: a case-control and cohort study

Wang, Y., Zhang, Q., Yin, C., Chen, L., Yang, Z., Jia, S., Sun, X., Bai, Y., Han, F., & Yuan, Z. (2022) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-022-04936-0 · Cited by 19 · see citing works

Scope of Practice: Freedom Within Limits

Schuiling, K., & Slager, J. (2000) · Journal of Midwifery & Women s Health · doi:10.1016/s1526-9523(00)00070-2 · Cited by 22 · see citing works

Dietetic Scopes of Practice Across Canada

Selinger, M., & Berenbaum, S. L. (2015) · Canadian Journal of Dietetic Practice and Research · doi:10.3148/cjdpr-2014-039

Modernizing Scope-of-Practice Regulations — Time to Prioritize Patients

Frogner, B. K., Fraher, E. P., Spetz, J., Pittman, P., Moore, J., Beck, A. J., Armstrong, D., & Buerhaus, P. I. (2020) · New England Journal of Medicine · doi:10.1056/nejmp1911077 · Cited by 61 · see citing works

The Scope of Professional Practice: A literature review to determine the document's impact on nurses' role

Finlay, T. M. D. (2000) · Journal of research in nursing · doi:10.1177/136140960000500208 · Cited by 1 · see citing works

Which factors are associated with attitudes of midwives toward full scope of practice?

Hoekstra, E., Spineli, L. M., & Groß, M. M. (2015) · Zeitschrift für Geburtshilfe und Neonatologie · doi:10.1055/s-0035-1566583